{"id":"W4387211866","doi":"10.1007/978-3-031-43898-1_21","title":"Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Image segmentation; Segmentation; Multivariate normal distribution; Pattern recognition (psychology); Contouring; Multivariate statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009445552,0.0007201119,0.001167128,0.0009929097,0.0003129999,0.001208778,0.001275157,0.001248986,0.002659881],"category_scores_gemma":[0.003940347,0.0007401364,0.0007923372,0.001161042,0.0006568982,0.001319287,0.001508208,0.001480746,0.0009508627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005842856,"about_ca_system_score_gemma":0.0004877485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000945365,"about_ca_topic_score_gemma":0.0009005175,"domain_scores_codex":[0.9994578,0.0001302629,0.00003056201,0.00009701811,0.0002494148,0.00003495235],"domain_scores_gemma":[0.9988943,0.0006447383,0.0001078472,0.0001579604,0.0001686474,0.00002653564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002442956,0.00004206416,0.0004105005,0.0003495582,0.0000993487,0.000149218,0.000101262,0.3044616,0.0470951,0.03884057,0.004185985,0.6040205],"study_design_scores_gemma":[0.000003527961,0.00002088951,0.0002186547,0.00002114337,0.00002061504,0.0001194282,0.000008432056,0.9737192,0.008188964,0.01531885,0.002347652,0.00001264786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00178739,0.0004839596,0.9968689,0.00004695318,0.00002018447,0.00001021263,0.00002705214,0.0001757067,0.0005796134],"genre_scores_gemma":[0.2052635,0.002052305,0.784952,0.000148253,0.0002153107,0.0001052988,0.0004393219,0.0005661229,0.006257988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002659881,"threshold_uncertainty_score":0.008898199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076881210962121,"score_gpt":0.3123579791594595,"score_spread":0.2915891670498382,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}